A VSLMS Style Tap-Length Learning Algorithm for structure adaptation

Compared with the ordinary adaptive filter, the variable-length adaptive filter is more efficient (including smaller computational complexity, lower power consumption and higher output SNR) because of its tap-length learning algorithm, which is able to dynamically adapt its tap-length to the optimal tap-length that best balances the complexity and the performance of the adaptive filter. Among existing tap-length algorithms, the LMS-style variable tap-length algorithm (also called fractional tap-length algorithm or FT algorithm) proposed by Y. Gong has the best performance because it has the fastest convergence rates and best stability. However, in some cases its performance deteriorates dramatically. To solve this problem, we first analyze the FT algorithm and point out some of its defects. Second, we propose a new FT algorithm called `VSLMS¿ (variable step-size LMS) style tap-length learning algorithm, which not only uses the concept of FT but also introduces a new concept of adaptive convergence slope. With this improvement the new FT algorithm has even faster convergence rates and better stability. Finally, we offer computer simulations to verify this improvement.